Create app.py

app.py

@@ -0,0 +1,189 @@
+import os
+import io
+import requests
+import joblib
+import pandas as pd
+import streamlit as st
+import plotly.graph_objects as go
+from datetime import datetime
+from supabase import create_client, Client
+import yfinance as yf
+
+# =====================================
+# ✅ CONFIGURATION
+# =====================================
+MODEL_URL = "https://huggingface.co/shaikfakruddin18/stock-predictor-model/resolve/main/rf_model.joblib"
+
+ALPHA_VANTAGE_API_KEY = " IY2HMVXFHXE83LB5"  # Replace with your API key
+SUPABASE_URL = "https://rrvsbizwikocatkdhyfs.supabase.co"
+SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6InJydnNiaXp3aWtvY2F0a2RoeWZzIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTI5NjExNDAsImV4cCI6MjA2ODUzNzE0MH0.YWP65KQvwna1yQlhjksyT9Rhpyn5bBw5MDlMVHTF62Q"
+
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+# Cache model so it's loaded only once
+@st.cache_resource
+def load_model_from_hf():
+    """Download model from Hugging Face and load it"""
+    model_path = "rf_model.joblib"
+    if not os.path.exists(model_path):
+        st.info("📥 Downloading ML model from Hugging Face...")
+        r = requests.get(MODEL_URL)
+        with open(model_path, "wb") as f:
+            f.write(r.content)
+    return joblib.load(model_path)
+
+model = load_model_from_hf()
+
+# =====================================
+# ✅ FETCH STOCK DATA
+# =====================================
+
+def fetch_yahoo_data(ticker, period="3mo"):
+    """Fetch historical daily data from Yahoo Finance"""
+    df = yf.download(ticker, period=period, interval="1d")
+    if df.empty:
+        return None
+    df.reset_index(inplace=True)
+    return df
+
+def fetch_alpha_vantage_intraday(ticker, interval="5min"):
+    """Fetch intraday data from Alpha Vantage"""
+    url = (
+        f"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY"
+        f"&symbol={ticker}&interval={interval}&apikey={ALPHA_VANTAGE_API_KEY}&datatype=json"
+    )
+    r = requests.get(url).json()
+    key = f"Time Series ({interval})"
+    if key not in r:
+        return None
+    df = pd.DataFrame(r[key]).T
+    df.columns = ["Open", "High", "Low", "Close", "Volume"]
+    df.index = pd.to_datetime(df.index)
+    df = df.sort_index()
+    df.reset_index(inplace=True)
+    df.rename(columns={"index": "Datetime"}, inplace=True)
+    df[["Open", "High", "Low", "Close", "Volume"]] = df[["Open", "High", "Low", "Close", "Volume"]].astype(float)
+    return df
+
+def fetch_supabase_csv(ticker):
+    """Fetch saved stock CSV from Supabase storage"""
+    try:
+        base_url = "https://rrvsbizwikocatkdhyfs.supabase.co/storage/v1/object/public/prediction/stock_data_with_indicators"
+        csv_url = f"{base_url}/{ticker}.csv"
+        df = pd.read_csv(csv_url)
+        return df
+    except:
+        return None
+
+# =====================================
+# ✅ PREDICTION + CHARTS
+# =====================================
+
+def predict_stock(df):
+    """Predict UP/DOWN using the loaded ML model"""
+    if df is None or df.empty:
+        return None, None
+
+    features = df[["Open", "High", "Low", "Close", "Volume"]].tail(1)  # Last row
+    pred = model.predict(features)[0]
+    confidence = model.predict_proba(features).max() * 100
+    prediction = "UP" if pred == 1 else "DOWN"
+    return prediction, confidence
+
+def plot_candlestick(df, title="Stock Price"):
+    fig = go.Figure(
+        data=[
+            go.Candlestick(
+                x=df[df.columns[0]],
+                open=df["Open"],
+                high=df["High"],
+                low=df["Low"],
+                close=df["Close"],
+            )
+        ]
+    )
+    fig.update_layout(title=title, xaxis_rangeslider_visible=False, height=400)
+    return fig
+
+def save_prediction_to_supabase(stock, prediction, confidence, source):
+    """Save prediction to Supabase DB"""
+    try:
+        created_at = datetime.utcnow().isoformat()
+        data = {
+            "created_at": created_at,
+            "stock": stock,
+            "prediction": prediction,
+            "confidence": f"{confidence:.2f}%",
+            "source": source
+        }
+        response = supabase.table("predictions").insert(data).execute()
+        if response.data:
+            st.success("✅ Prediction saved to Supabase!")
+        else:
+            st.error(f"❌ Failed to save prediction: {response}")
+    except Exception as e:
+        st.error(f"❌ Supabase error: {e}")
+
+def load_prediction_history_supabase():
+    """Load previous predictions"""
+    try:
+        response = supabase.table("predictions").select("*").order("created_at", desc=True).execute()
+        return pd.DataFrame(response.data) if response.data else pd.DataFrame()
+    except Exception as e:
+        st.error(f"❌ Failed to load history: {e}")
+        return pd.DataFrame()
+
+# =====================================
+# ✅ STREAMLIT UI
+# =====================================
+
+st.set_page_config(page_title="AI Stock Predictor", layout="wide")
+st.sidebar.title("📊 Navigation")
+
+st.sidebar.subheader("Select Data Source")
+data_source = st.sidebar.radio(
+    "Fetch data from:",
+    ["Yahoo Finance (Daily)", "Alpha Vantage (Intraday)", "Supabase CSV"]
+)
+
+ticker = st.sidebar.text_input("Enter Stock Ticker (e.g. AAPL, RELIANCE.BSE)", "AAPL")
+
+if data_source == "Yahoo Finance (Daily)":
+    period = st.sidebar.selectbox("Select Period", ["1mo", "3mo", "6mo", "1y", "2y"], index=1)
+elif data_source == "Alpha Vantage (Intraday)":
+    interval = st.sidebar.selectbox("Intraday Interval", ["1min", "5min", "15min", "30min", "60min"], index=1)
+
+st.title("📈 AI Stock Predictor Dashboard")
+
+if st.sidebar.button("Fetch Data & Predict"):
+    if data_source == "Yahoo Finance (Daily)":
+        df = fetch_yahoo_data(ticker, period)
+        source_name = "YahooFinance"
+    elif data_source == "Alpha Vantage (Intraday)":
+        df = fetch_alpha_vantage_intraday(ticker, interval)
+        source_name = "AlphaVantage"
+    else:
+        df = fetch_supabase_csv(ticker)
+        source_name = "Supabase CSV"
+
+    if df is None or df.empty:
+        st.error("❌ No data returned. Check ticker or date range.")
+    else:
+        st.subheader(f"Stock Data: {ticker} ({source_name})")
+        st.plotly_chart(plot_candlestick(df, f"{ticker} Price Chart"), use_container_width=True)
+
+        prediction, confidence = predict_stock(df)
+        if prediction:
+            st.markdown(f"### Prediction: **{prediction}**")
+            st.markdown(f"### Confidence: **{confidence:.2f}%**")
+            save_prediction_to_supabase(ticker, prediction, confidence, source_name)
+        else:
+            st.warning("⚠️ Could not generate prediction.")
+
+# Show Prediction History
+st.subheader("📜 Prediction History (Cloud)")
+history_df = load_prediction_history_supabase()
+if not history_df.empty:
+    st.dataframe(history_df[["created_at", "stock", "prediction", "confidence", "source"]])
+else:
+    st.info("No prediction history yet.")